Permanently delete a Blueprint and revoke its API keys. Irreversible; requires confirm=true. Account-level keys are unaffected.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | GeodesicAI API key (gai_...) | |
| confirm | No | Must be true to confirm this irreversible action | |
| workflow_name | Yes | Blueprint to delete; its API keys are revoked |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses irreversibility ('Irreversible'), the confirmation requirement ('requires confirm=true'), and the side effect of revoking the blueprint's API keys. These details go beyond the annotations, which only indicate destructive and idempotent hints, adding valuable behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences, front-loaded with the primary action. No wasted words; each sentence adds necessary context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple delete operation with full schema coverage and no output schema, the description covers the key aspects: what is deleted, the irreversibility, the confirmation requirement, and the scope relative to account-level keys. No critical information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides descriptions for all 3 parameters (100% coverage), including confirm's 'Must be true to confirm this irreversible action'. The tool description adds no new parameter semantics beyond reiterating the confirm requirement, so it stays at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Permanently delete a Blueprint and revoke its API keys.' This distinguishes it from sibling tools like delete_api_key and update_blueprint, and notes account-level keys are unaffected, clarifying scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly name alternatives, but the note 'Account-level keys are unaffected' implies that account-level key deletion should use delete_api_key. It also states the requirement 'requires confirm=true', providing a clear precondition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but several pairs overlap heavily (validate vs validate_repair, repair vs repair_path, analyze_anomaly vs check_drift vs decompose_failure). Detailed descriptions help disambiguate, but the large number of analytics and diagnostics tools creates real selection risk.
The vast majority use a consistent snake_case verb_noun pattern (create_blueprint, list_api_keys, verify_certificate). A few single-word or noun-phrase exceptions (validate, forecast, structural_types, recent_inference_decisions) are minor deviations, but overall the pattern is predictable.
At 37 tools, this exceeds the 25+ threshold for 'too many'. While the governance domain is broad, the set could be consolidated (e.g., merging validate_repair into validate, folding repair_path into repair, or trimming diagnostics-tier tools like check_realization and geometric_confidence).
The surface covers the full blueprint lifecycle, validation, repair, API key management, discovery, inference governance, and chain management. Minor gaps exist: no direct get_blueprint (only list with counts), and chain lifecycle lacks delete/list/cancel operations. Overall, agents can accomplish core governance tasks without dead ends.
